From fields to objects: A review of geographic boundary analysis

Geographic boundary analysis is a relatively new approach unfamiliar to many spatial analysts. It is best viewed as a technique for defining objects – geographic boundaries – on spatial fields, and for evaluating the statistical significance of characteristics of those boundary objects. This is accomplished using null spatial models representative of the spatial processes expected in the absence of boundary-generating phenomena. Close ties to the object-field dialectic eminently suit boundary analysis to GIS data. The majority of existing spatial methods are field-based in that they describe, estimate, or predict how attributes (variables defining the field) vary through geographic space. Such methods are appropriate for field representations but not object representations. As the object-field paradigm gains currency in geographic information science, appropriate techniques for the statistical analysis of objects are required. The methods reviewed in this paper are a promising foundation. Geographic boundary analysis is clearly a valuable addition to the spatial statistical toolbox.? This paper presents the philosophy of, and motivations for geographic boundary analysis. It defines commonly used statistics for quantifying boundaries and their characteristics, as well as simulation procedures for evaluating their significance. We review applications of these techniques, with the objective of making this promising approach accessible to the GIS-spatial analysis community. We also describe the implementation of these methods within geographic boundary analysis software: GEM. Received: 22 March 1999 / Accepted: 7 September 1999     

矢量与栅格集成的三维数据模型

以矿山地质为背景,深入分析三维空间信息系统所涉及到的空间对象以及它们之间的联系,提出了几种新的空间对象类型。探讨用矢量与栅格混合的数据结构,以及面向对象的数据模型来表达各类三维空间对象,以此作为设计和建立三维地理信息系统的基础。     

An integrated data model in three dimensional GIS

The current GIS can only deal with 2-D or 2.5-D information on the earth surface. A new 3-D data structure and data model need to be designed for the 3-D GIS. This paper analyzes diverse 3-D spatial phenomena from mine to geology and their complicated relations, and proposes several new kinds of spatial objects including cross-section, column body and digital surface model to represent some special spatial phenomena like tunnels and irregular surfaces of an ore body. An integrated data structure including vector, raster and object-oriented data models is used to represent various 3-D spatial objects and their relations. The integrated data structure and object-oriented data model can be used as bases to design and realize a 3-D geographic information system.     

Spatial Analysis of 1991 Gulf War Troop Locations in Relationship with Postwar Health Symptom Reports Using GIS Techniques

Spatial autocorrelation analysis was used to identify spatial patterns of 1991 Gulf War (GW) troop locations in relationship to subsequent postwar diagnosis of chronic multisymptom illness (CMI). Criteria for the diagnosis of CMI include reporting from at least two of three symptom clusters: fatigue, musculoskeletal pain, and mood and cognition. A GIS‐based methodology was used to examine associations between potential hazardous exposures or deployment situations and postwar health outcomes using troop location data as a surrogate. GW veterans from the Devens Cohort Study were queried about specific symptoms approximately four years after the 1991 deployment to the Persian Gulf. Global and local statistics were calculated using the Moran's I and G statistics for six selected date periods chosen a priori to mark important GW‐service events or exposure scenarios among 173 members of the cohort. Global Moran's I statistics did not detect global spatial patterns at any of the six specified data periods, thus, indicating there is no significant spatial autocorrelation of locations over the entire Gulf region for veterans meeting criteria for severe postwar CMI. However, when applying local G* and local Moran's I statistics, significant spatial clusters (primarily in the coastal Dammam/Dharhan and the central inland areas of Saudi Arabia) were identified for several of the selected time periods. Further study using GIS techniques, coupled with epidemiological methods, to examine spatial and temporal patterns with larger sample sizes of GW veterans is warranted to ascertain if the observed spatial patterns can be confirmed.     

点集数据不规则形状时空异常聚类模式挖掘研究

传统扫描统计方法在进行时空异常聚类模式挖掘时,受扫描窗口形状的限制,不能准确地获取聚类区域形状。提出一种改进的不规则形状时空异常聚类模式挖掘方法stAntScan。新方法基于26方位时空邻近单元格构建时空邻接矩阵,再对蚁群最优化扫描统计方法进行改进,使其能适应三维大数据量的时空区域扫描。模拟数据和真实微博签到数据的实验证明,stAntScan能有效地识别时空范围内的不规则形状异常聚类,并且准确性较经典的SaTScan方法高。     

Spatial signatures for geographic feature types: examining gazetteer ontologies using spatial statistics

Digital gazetteers play a key role in modern information systems and infrastructures. They facilitate (spatial) search, deliver contextual information to recommended systems, enrich textual information with geographical references, and provide stable identifiers to interlink actors, events, and objects by the places they interact with. Hence, it is unsurprising that gazetteers, such as GeoNames, are among the most densely interlinked hubs on the Web of Linked Data. A wide variety of digital gazetteers have been developed over the years to serve different communities and needs. These gazetteers differ in their overall coverage, underlying data sources, provided functionality, and geographic feature type ontologies. Consequently, place types that share a common name may differ substantially between gazetteers, whereas types labeled differently may, in fact, specify the same or similar places. This makes data integration and federated queries challenging, if not impossible. To further complicate the situation, most popular and widely adopted geo‐ontologies are lightweight and thus under‐specific to a degree where their alignment and matching become nothing more than educated guesses. The most promising approach to addressing this problem, and thereby enabling the meaningful integration of gazetteer data across feature types, seems to be a combination of top‐down knowledge representation with bottom‐up data‐driven techniques such as feature engineering and machine learning. In this work, we propose to derive indicative spatial signatures for geographic feature types by using spatial statistics. We discuss how to create such signatures by feature engineering and demonstrate how the signatures can be applied to better understand the differences and commonalities of three major gazetteers, namely DBpedia Places, GeoNames, and TGN.     

基于空间统计学的空间数据窗口大小的确定

提出基于空间统计学的方法来确定空间数据窗口大小,实例证明是可行的。同时提出了对海底照片成像不均匀光照进行纠正的思路和方法,该方法简单有效,效果较理想。     

Spatially Clustered Associations in Health Related Geospatial Data

Overlaying maps using a desktop GIS is often the first step of a multivariate spatial analysis. The potential of this operation has increased considerably as data sources and Web services to manipulate them are becoming widely available via the Internet. Standards from the OGC enable such geospatial ‘mashups’ to be seamless and user driven, involving discovery of thematic data. The user is naturally inclined to look for spatial clusters and ‘correlation’ of outcomes. Using classical cluster detection scan methods to identify multivariate associations can be problematic in this context, because of a lack of control on or knowledge about background populations. For public health and epidemiological mapping, this limiting factor can be critical but often the focus is on spatial identification of risk factors associated with health or clinical status. In this article we point out that this association itself can ensure some control on underlying populations, and develop an exploratory scan statistic framework for multivariate associations. Inference using statistical map methodologies can be used to test the clustered associations. The approach is illustrated with a hypothetical data example and an epidemiological study on community MRSA. Scenarios of potential use for online mashups are introduced but full implementation is left for further research.     

基于基础地理数据统计的长三角城市主城区空间格局扩展研究

在快速城镇化时期,城市主城区的空间格局变化最为显著,以其复杂性和扩展性为突出特征。研究其扩展变化对认识城镇化、优化城市空间结构等具有重要意义。本文在基础地理数据统计的基础上,对长三角城市主城区2002~2012年的空间扩展进行了分析,探索影响其演变的若干因素。     

Ontology‐based Qualitative Feature Analysis: Bays as a Case Study

A methodology for analyzing geographic data using the techniques of: (1) qualitative geometric abstraction; and (2) ontological analysis of geographic features is described. The first technique is a bottom‐up approach to extract qualitative spatial relations from geographic representations (raster or vector) while the second technique is a top‐down approach to determine which qualitative relations can possibly hold between the parts of the geographic features. The process of analyzing geographic data includes the extraction of both the features and the qualitative relations among features. These qualitative relations are then used to classify the geographic features within the “space” of ontological possibilities. In this article bays in Wisconsin and their cartographic representation are used as a running example and the subject of a case study.     

基于不规则网格的城市管理网格体系与地理编码

从网格的定义出发,论述了规则网格与不规则网格的特点,描述了等级不规则网格的组成,指出地籍是城市最小不规则空间网格单元,利用地籍可以组合成其他管理分区(网格),使城市空间信息的纵向综合与横向共享得以实现,最后探讨了基于等级不规则网格的地理编码标准。     

Visualising Crime Clusters in a Space‐time Cube: An Exploratory Data‐analysis Approach Using Space‐time Kernel Density Estimation and Scan Statistics

For an effective interpretation of spatio‐temporal patterns of crime clusters/hotspots, we explore the possibility of three‐dimensional mapping of crime events in a space‐time cube with the aid of space‐time variants of kernel density estimation and scan statistics. Using the crime occurrence dataset of snatch‐and‐run offences in Kyoto City from 2003 to 2004, we confirm that the proposed methodology enables simultaneous visualisation of the geographical extent and duration of crime clusters, by which stable and transient space‐time crime clusters can be intuitively differentiated. Also, the combined use of the two statistical techniques revealed temporal inter‐cluster associations showing that transient clusters alternatively appeared in a pair of hotspot regions, suggesting a new type of “displacement” phenomenon of crime. Highlighting the complementary aspects of the two space‐time statistical approaches, we conclude that combining these approaches in a space‐time cube display is particularly valuable for a spatio‐temporal exploratory data analysis of clusters to extract new knowledge of crime epidemiology from a data set of space‐time crime events.     

A test for the coincident economic and spatial clustering of business enterprises

This paper uses input-output data combined with point process modeling techniques to test whether enterprises linked within nominal buyer-supplier chains have a greater propensity to cluster in space than manufacturing enterprises in general. The methodology controls for the general tendency of firms to seek locations in concentrated agglomerations and isolates the influence of firm interdependence on spatial clustering. Our findings suggest that there is indeed an association between economic linkages and geographic clustering in our study area, but only for some types of economic clusters, mainly those that are comprised mainly of more knowledge-based or technology-intensive sectors. In general, we endeavor to show that spatial analytical methods hold considerable promise for conducting rigorous tests of industrial location questions. Received: 9 September 1998 / Accepted: 12 December 1999     

空间层次聚类显著性判别的重排检验方法

同时顾及空间邻近与专题属性相似的空间层次聚类是挖掘空间分布模式的一种有效手段。空间层次聚类方法虽然可以获得多层次的聚集结构,但聚类结果显著性的统计判别依然是一个尚未解决的难题。为此,本文提出了一种空间层次聚类结果显著性的统计判别方法,用于确定空间层次聚类的停止准则,减少聚类过程对参数设置的依赖。通过试验分析与比较发现,该方法能够有效判别空间层次聚类结果的显著性和确定层次聚类合并过程的停止条件,同时具有很好的抗噪性,避免随机结构的干扰。     

利用重力模型进行GIS点群选取

在现有GIS点状要素综合选取方法的基础上,针对两种具有相关关系的GIS要素,综合考虑它们的空间与属性信息,运用重力模型对要素间的相关性进行评价,提出惯性选取的方法。实验结果表明,重力模型可以很好地集成地理目标的空间与属性信息,定量描述不同要素之间的相关性;采取惯性选取的方法不仅可以保持目标综合前后空间结构的一致性,而且可以顾及不同地理要素相关性对目标选取的影响。     

Enhancing Operations with Spatial Access Methods in a Database Management System for GIS

Much attention has been devoted in the past to support classes of applications which are not well served by conventional database systems. Focusing on the application domain of geographic information systems (GIS), several architectural approaches have been proposed to implement commercial or prototype systems and satisfy the urgent needs for geographic data handling. However, those systems suffer from several limitations because they either perform much processing on an application layer, which is at the top of the database management system (DBMS), or the underlying data models are not rich enough to represent the spatial dimension of geographic entities. This study examines the spatial operations that should be provided by a DBMS for the application domain of GIS and focuses on the various techniques which may be used to support the efficient execution of both simple operations and composite procedures that involve the spatial dimension of geographic entities.     

基于扫描统计的甲型H1N1流感聚集性分析

扫描统计已被广泛应用于地域性疾病的聚集性检测,且可检测这种聚集的差异显著性。文中使用扫描统计的方法,通过对2009~2012年全国各省的甲型H1N1流感数据进行时空扫描以及逐年的空间扫描,生成高发病率地区和低发病率地区聚类,并通过叠置分析反映各个地区归入聚类的频次。利用专题地图和统计表分析甲型流感在2009~2012年中的爆发情况和趋势,并对结果进行客观的分析。     

Homological relations: A methodology for the certification of irregular tessellations

In GIS, spatial analysis is based on the use of spatial operations such as testing the spatial relations between features. Often, such tests are invalidated by errors in datasets. It is a very common experience that two bordering regions which should obey the topological relation “meet” fall instead in the “overlap” category. The situation is exacerbated when applying topological operators to regions that come from different datasets, where resolution and error sources are different. Despite the problem being quite common, up to now no standard approach has been defined to deal with spatial relations affected by errors of various origins. Referring to topological relations, we define a model to extend the eight Egenhofer relations between two simple regions: we call them homological relations (H‐relations). We discuss how exact topological relations can be extracted from observed relations and discuss the case of irregular tessellations, where errors have the most impact on vector data. In the proposed case study within the domain of geographic crowdsourced data, we propose algorithms for identifying homological regions and obtaining a corrected tessellation. This methodology can be considered as a step for quality control and the certification of irregular tessellations.     

Modeling spatial dependence in high spatial resolution hyperspectral data sets

 As either the spatial resolution or the spatial scale for a geographic landscape increases, both latent spatial dependence and spatial heterogeneity also will tend to increase. In addition, the amount of georeferenced data that results becomes massively large. These features of high spatial resolution hyperspectral data present several impediments to conducting a spatial statistical analysis of such data. Foremost is the requirement of popular spatial autoregressive models to compute eigenvalues for a row-standardized geographic weights matrix that depicts the geographic configuration of an image's pixels. A second drawback arises from a need to account for increased spatial heterogeneity. And a third concern stems from the usefulness of marrying geostatistical and spatial autoregressive models in order to employ their combined power in a spatial analysis. Research reported in this paper addresses all three of these topics, proposing successful ways to prevent them from hindering a spatial statistical analysis. For illustrative purposes, the proposed techniques are employed in a spatial analysis of a high spatial resolution hyperspectral image collected during research on riparian habitats in the Yellowstone ecosystem. Received: 25 February 2001 / Accepted: 2 August 2001     

ADCN: An anisotropic density‐based clustering algorithm for discovering spatial point patterns with noise

Density‐based clustering algorithms such as DBSCAN have been widely used for spatial knowledge discovery as they offer several key advantages compared with other clustering algorithms. They can discover clusters with arbitrary shapes, are robust to noise, and do not require prior knowledge (or estimation) of the number of clusters. The idea of using a scan circle centered at each point with a search radius Eps to find at least MinPts points as a criterion for deriving local density is easily understandable and sufficient for exploring isotropic spatial point patterns. However, there are many cases that cannot be adequately captured this way, particularly if they involve linear features or shapes with a continuously changing density, such as a spiral. In such cases, DBSCAN tends to either create an increasing number of small clusters or add noise points into large clusters. Therefore, in this article, we propose a novel anisotropic density‐based clustering algorithm (ADCN). To motivate our work, we introduce synthetic and real‐world cases that cannot be handled sufficiently by DBSCAN (or OPTICS). We then present our clustering algorithm and test it with a wide range of cases. We demonstrate that our algorithm can perform equally as well as DBSCAN in cases that do not benefit explicitly from an anisotropic perspective, and that it outperforms DBSCAN in cases that do. Finally, we show that our approach has the same time complexity as DBSCAN and OPTICS, namely O(n log n) when using a spatial index and O(n²) otherwise. We provide an implementation and test the runtime over multiple cases.